Adaptive Heart Rate Estimation from Face Videos
We propose a novel heart rate (HR) estimation method from facial videos that dynamically adapts the HR pulse extraction algorithm to separately deal with noise from 'rigid' head motion and 'non-rigid' facial expression. We first identify the noise type, based on which, we apply specific noise removal steps. Experiments performed on popular database show that the proposed method reduces HR estimation error by over 32%.
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